![]() ![]() ![]() The second step is to calculate the comparables’ lot values based on their size. This is simply the difference between effective date and the comparable’s date of sale multiplied by the determined price trend for the market area. Then linear regression is used to calculate the relationship between GLA and bare price, producing the “m” GLA adjustment factor.įor the high impact variables, the first step is to calculate the impact of Date of Sale on the sales prices of the considered comparables. Essentially, for each potential comparable, the sales price is modified by applying the high impact and low impact adjustments to the sales price to arrive at a bare price that is a function of GLA only. The high impact variable relationships are calculated independently and the low impact adjustments are assigned based upon experience and/or prior matched pair analysis. Basically, statistical analysis is used to determine the relationship between price and GLA and takes the form of y = mx + b or m = (y – b)/x where “m” is the slope or GLA adjustment factor, “y” is the bare price and “b” is a constant. The process is much less complicated than multiple regression and can be done using a simple spreadsheet. Unlike matched pair, a reasonably-sized population of comparables is analyzed. When this happens, it seems to me that the validity of the results is also compromised.Īnother approach to arriving at a supportable GLA adjustment factor is linear regression. The re-run results can then be used as the adjustment factors in the sales comparison grid, along with the appraiser’s opinions for the omitted adjustment factors. The fix for this is to re-run the analysis without the offending parameters. The reason is that they are overwhelmed by the high impact parameters (GLA, date of sale, lot size differences) and/or unquantified parameters such as condition and quality. This happens most often with low impact parameters (independent variables) such as bath count and garage spaces. In most cases when I’ve used this method, multiple regression produces one or more adjustment factors (regression coefficients) that are negative when they should be positive. For example, date of sale, lot size, GLA, stories, age, bath count, garage spaces, fireplace, pool & spa etc. Multiple regression analysis is another method that should be the gold standard as it uses statistical analysis to produce adjustment factors for a whole range of parameters. There are almost always differences in lot size, condition and other features that can compromise the validity of this approach. However, this is an ideal situation that is rarely encountered. ![]() ![]() Given two properties with different GLAs, but otherwise identical, in the same neighborhood on equivalent lots and with the same date of sale, the adjustment factor is calculated as the difference in sales prices is divided by the difference in GLAs. Matched pair analysis is one acceptable method. I have, however, done a certain amount of research on methods of arriving at GLA adjustments based on data analysis as opposed to rules of thumb. Most of the appraisals I have prepared have not included support, either. Over the past several years, I have reviewed dozens of appraisals and I can’t recall one instance where the appraiser has included such support. However, the production of reasonable results does not satisfy the requirement to provide support for adjustment factors. For the most part, it seemed to produce reasonable results. “One of the best courses that I have had in 17 years!”Įducation designed to help you appraise worry-free and earn higher fees.Ī Spreadsheet Solution for Estimating GLA AdjustmentsĪs an appraiser trainee, I was taught to calculate GLA adjustments by averaging the comparables’ sales price per square foot and multiplying it by 50%, up to a maximum of about $80/sq. ![]()
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